-------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  [reducted]
  log type:  text
 opened on:   9 Apr 2021, 12:52:49

. 
. do analysis.do

. * note -------------------------------------------------------------------------
. 
. * this is one of the two master replication files for:
. *
. * umit, r. rallies around the flag-draped coffins: the electoral effects of 
. * security force casualties in terror attacks. conditionally accepted (pending 
. * replication) for publication in political science research and methods.
. *
. * the other master replication file is called analysis.R 
. *
. * replication requires the following directory structure:
. *
. * working_directory/
. *  |
. *  +---analysis.do
. *  +---analysis.R
. *  |
. *  +---datasets/
. *  |       data_*
. *  |
. *  +---figures/
. *  |       
. *  |
. *  +---scripts/
. *  |       script_*
. *  |
. *  \---tables/
. *
. * replication also requires two third-party packages, which can be installed by
. * removing the leading asterisks from the lines 38 and 39 below.
. *
. * declare the stata release used -----------------------------------------------
. 
. version 16.1

. 
. * install the required third-party packages ------------------------------------
. 
. * ssc install estout
. * ssc install ebalance
. 
. * import the fdc dataset -------------------------------------------------------
. 
. import delimited "./datasets/data_fdc.csv"
(31 vars, 970 obs)

. 
. * label the variables in the main text -----------------------------------------
. 
. label variable akp_nov             "Post-test"

. 
. label variable treatment           "Treatment"

. label variable treatment_multi     "Multiple Treatment"

. 
. label variable akp_jun             "Pre-test"

. label variable recruitment_pool    "Recruitment Pool" 

. 
. label variable treatment_nonterror "Non-terror Funeral"

. label variable casualty_district   "Attack District"

. label variable kurdish_district    "Kurdish District"

. label variable akp_district        "AKP District"

. label variable higher_education    "Higher Education"

. label variable min_margin          "Electoral Margin"

. label variable turnout_jun         "Turnout"

. 
. * source the script that creates table 1 ---------------------------------------
. 
. do "./scripts/script_table_1.do"

. * note -------------------------------------------------------------------------
. 
. * in the main text, table 1 displays a summary of the output to be created below
. * the complete results are displayed in the supporting information (table s3)
. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov treatment akp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    1687.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9596
                                                Root MSE          =     3.8224

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   1.028215    .411632     2.50   0.015     .2090412    1.847389
         akp_jun |   1.084056   .0157499    68.83   0.000     1.052713    1.115399
recruitment_pool |   .0198922   .0151153     1.32   0.192    -.0101882    .0499726
           _cons |   4.589535   .8777807     5.23   0.000     2.842695    6.336374
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov treatment akp_jun recruitment_pool                          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     912.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9694
                                                Root MSE          =     3.3377

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.088677   .3651188     2.98   0.004     .3620676    1.815287
            akp_jun |    1.06079   .0155556    68.19   0.000     1.029833    1.091746
   recruitment_pool |   .0507082   .0157006     3.23   0.002      .019463    .0819534
treatment_nonterror |  -.5234295   .4052526    -1.29   0.200    -1.329908    .2830489
  casualty_district |  -.9695885   .9762579    -0.99   0.324    -2.912404    .9732266
   kurdish_district |   3.293495   .7638653     4.31   0.000     1.773355    4.813636
       akp_district |   2.075306   .3162353     6.56   0.000     1.445978    2.704634
   higher_education |  -.1195369   .0363287    -3.29   0.001    -.1918333   -.0472405
         min_margin |   .0170475   .0341554     0.50   0.619     -.050924     .085019
        turnout_jun |  -.1477943   .0517829    -2.85   0.005    -.2508456    -.044743
              _cons |   17.00327   4.681051     3.63   0.000     7.687679    26.31885
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov treatment treatment_multi akp_jun recruitment_pool,         ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1465.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9597
                                                Root MSE          =     3.8194

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   1.256686    .470101     2.67   0.009     .3211549    2.192216
 treatment_multi |  -1.471472   .7160207    -2.06   0.043    -2.896398    -.046545
         akp_jun |    1.08393   .0157353    68.89   0.000     1.052616    1.115244
recruitment_pool |   .0229247   .0158675     1.44   0.152    -.0086526     .054502
           _cons |   4.578108   .8749415     5.23   0.000     2.836919    6.319297
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov treatment treatment_multi akp_jun recruitment_pool          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     822.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3362

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.260685   .4073108     3.10   0.003     .4501104    2.071259
    treatment_multi |  -1.121329   .5095216    -2.20   0.031    -2.135309   -.1073488
            akp_jun |   1.060637   .0154344    68.72   0.000     1.029921    1.091352
   recruitment_pool |    .052969   .0165071     3.21   0.002     .0201189    .0858192
treatment_nonterror |  -.5018725    .396568    -1.27   0.209    -1.291068     .287323
  casualty_district |  -1.006431    .978949    -1.03   0.307    -2.954601    .9417399
   kurdish_district |   3.287623   .7553005     4.35   0.000     1.784528    4.790719
       akp_district |   2.068025   .3156711     6.55   0.000     1.439819     2.69623
   higher_education |  -.1186921   .0359752    -3.30   0.001     -.190285   -.0470993
         min_margin |    .017726   .0340341     0.52   0.604     -.050004    .0854561
        turnout_jun |  -.1486963   .0519929    -2.86   0.005    -.2521655   -.0452271
              _cons |   17.07216    4.69673     3.63   0.000     7.725375    26.41895
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table 1: Regression models of government vote share")

Table 1: Regression models of government vote share
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   1.028*          1.089**         1.257**         1.261** 
                          (0.412)         (0.365)         (0.470)         (0.407)   

Multiple Treatment                                         -1.471*         -1.121*  
                                                          (0.716)         (0.510)   

Pre-test                    1.084***        1.061***        1.084***        1.061***
                          (0.016)         (0.016)         (0.016)         (0.015)   

Recruitment Pool            0.020           0.051**         0.023           0.053** 
                          (0.015)         (0.016)         (0.016)         (0.017)   

Non-terror Funeral                         -0.523                          -0.502   
                                          (0.405)                         (0.397)   

Attack District                            -0.970                          -1.006   
                                          (0.976)                         (0.979)   

Kurdish District                            3.293***                        3.288***
                                          (0.764)                         (0.755)   

AKP District                                2.075***                        2.068***
                                          (0.316)                         (0.316)   

Higher Education                           -0.120**                        -0.119** 
                                          (0.036)                         (0.036)   

Electoral Margin                            0.017                           0.018   
                                          (0.034)                         (0.034)   

Turnout                                    -0.148**                        -0.149** 
                                          (0.052)                         (0.052)   

Constant                    4.590***       17.003***        4.578***       17.072***
                          (0.878)         (4.681)         (0.875)         (4.697)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.960           0.969           0.960           0.970   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_1.tex",                                            ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of government vote share")
(output written to ./tables/table_1.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s1 --------------------------------------
. 
. do "./scripts/script_table_s1.do"

. * calculate and save the statistics to memory ----------------------------------
. 
. eststo: estpost tabstat akp_nov treatment treatment_multi akp_jun               ///
> recruitment_pool treatment_nonterror casualty_district kurdish_district         ///
> akp_district higher_education min_margin turnout_jun,                           ///             
> statistics(mean median sd min max) columns(statistics)

Summary statistics: mean p50 sd min max
     for variables: akp_nov treatment treatment_multi akp_jun recruitment_pool treatment_nonterror casualty_district kurdish_district akp_district higher_education
>  min_margin turnout_jun

             |   e(mean)     e(p50)      e(sd)     e(min)     e(max) 
-------------+-------------------------------------------------------
     akp_nov |  52.97227       54.7   18.99674          2       95.9 
   treatment |  .1268041          0   .3329251          0          1 
treatment_~i |  .0216495          0   .1456113          0          1 
     akp_jun |  44.39041       45.6   17.18358        1.2       90.7 
recruitmen~l |  6.568096     2.2515   10.84789       .093     88.248 
treatment_~r |  .0371134          0   .1891372          0          1 
casualty_d~t |  .0484536          0   .2148335          0          1 
kurdish_di~t |  .1845361          0   .3881209          0          1 
akp_district |   .628866          1   .4833574          0          1 
higher_edu~n |  7.270129   6.225763   4.343841   1.462869   36.85306 
  min_margin |  4.743664   2.864901   5.155755    .066138    32.6368 
 turnout_jun |  85.58701       86.2   4.227737         67       98.8 
(est1 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> cells("mean(fmt(2)) sd(fmt(2)) p50(fmt(2)) min(fmt(2)) max(fmt(2))")            ///
> noobs label nonumber title("Table S1: Descriptive statistics")                             

Table S1: Descriptive statistics
-------------------------------------------------------------------------------------
                                                                                     
                             mean           sd          p50          min          max
-------------------------------------------------------------------------------------
Post-test                   52.97        19.00        54.70         2.00        95.90
Treatment                    0.13         0.33         0.00         0.00         1.00
Multiple Treatment           0.02         0.15         0.00         0.00         1.00
Pre-test                    44.39        17.18        45.60         1.20        90.70
Recruitment Pool             6.57        10.85         2.25         0.09        88.25
Non-terror Funeral           0.04         0.19         0.00         0.00         1.00
Attack District              0.05         0.21         0.00         0.00         1.00
Kurdish District             0.18         0.39         0.00         0.00         1.00
AKP District                 0.63         0.48         1.00         0.00         1.00
Higher Education             7.27         4.34         6.23         1.46        36.85
Electoral Margin             4.74         5.16         2.86         0.07        32.64
Turnout                     85.59         4.23        86.20        67.00        98.80
-------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s1.tex", replace                                   ///
> cells("mean(fmt(2)) sd(fmt(2)) p50(fmt(2)) min(fmt(2)) max(fmt(2))")            ///
> noobs label nonumber title("Descriptive statistics")                                         
(output written to ./tables/table_s1.tex)

. 
. * remove the statistics from memory --------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s2 --------------------------------------
. 
. do "./scripts/script_table_s2.do"

. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg treatment recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(1, 80)          =       5.78
                                                Prob > F          =     0.0185
                                                R-squared         =     0.0538
                                                Root MSE          =     .32402

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
       treatment |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
recruitment_pool |   .0071173   .0029594     2.41   0.018      .001228    .0130066
           _cons |   .0800571   .0130465     6.14   0.000     .0540937    .1060204
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg treatment recruitment_pool akp_jun kurdish_district akp_district    ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(7, 80)          =       2.14
                                                Prob > F          =     0.0482
                                                R-squared         =     0.0578
                                                Root MSE          =     .32434

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
       treatment |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
recruitment_pool |    .007451   .0033217     2.24   0.028     .0008406    .0140613
         akp_jun |   .0012467   .0007871     1.58   0.117    -.0003197    .0028132
kurdish_district |  -.0079623   .0395197    -0.20   0.841    -.0866091    .0706844
    akp_district |  -.0110489   .0278001    -0.40   0.692    -.0663728     .044275
higher_education |   .0001925   .0031129     0.06   0.951    -.0060023    .0063874
      min_margin |    .000585   .0021263     0.28   0.784    -.0036464    .0048164
     turnout_jun |  -.0009249   .0030669    -0.30   0.764    -.0070283    .0051786
           _cons |   .1059215   .3008231     0.35   0.726    -.4927356    .7045786
----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg treatment_multi recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(1, 80)          =       4.91
                                                Prob > F          =     0.0295
                                                R-squared         =     0.0563
                                                Root MSE          =     .14152

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
 treatment_multi |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
recruitment_pool |   .0031862    .001438     2.22   0.030     .0003246    .0060479
           _cons |   .0007219   .0059772     0.12   0.904     -.011173    .0126169
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg treatment_multi recruitment_pool akp_jun kurdish_district           ///
> akp_district higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(7, 80)          =       1.24
                                                Prob > F          =     0.2896
                                                R-squared         =     0.0595
                                                Root MSE          =     .14172

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
 treatment_multi |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
recruitment_pool |   .0031096   .0014404     2.16   0.034      .000243    .0059761
         akp_jun |   .0001246   .0004547     0.27   0.785    -.0007803    .0010296
kurdish_district |  -.0111229   .0184751    -0.60   0.549    -.0478895    .0256437
    akp_district |  -.0075252   .0102835    -0.73   0.466    -.0279901    .0129396
higher_education |    .001002   .0012093     0.83   0.410    -.0014046    .0034085
      min_margin |   .0005779    .000826     0.70   0.486     -.001066    .0022217
     turnout_jun |  -.0010085   .0009596    -1.05   0.296    -.0029182    .0009012
           _cons |   .0787676   .1032871     0.76   0.448    -.1267802    .2843155
----------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> mgroups("Treatment" "Multiple Treatment",                                       ///
> pattern(1 0 1 0) span)                                                          ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S2: Regression models of treatment allocation")

Table S2: Regression models of treatment allocation
------------------------------------------------------------------------------------
                     Treatment                       Multiple Treatment             
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Recruitment Pool            0.007*          0.007*          0.003*          0.003*  
                          (0.003)         (0.003)         (0.001)         (0.001)   

Pre-test                                    0.001                           0.000   
                                          (0.001)                         (0.000)   

Kurdish District                           -0.008                          -0.011   
                                          (0.040)                         (0.018)   

AKP District                               -0.011                          -0.008   
                                          (0.028)                         (0.010)   

Higher Education                            0.000                           0.001   
                                          (0.003)                         (0.001)   

Electoral Margin                            0.001                           0.001   
                                          (0.002)                         (0.001)   

Turnout                                    -0.001                          -0.001   
                                          (0.003)                         (0.001)   

Constant                    0.080***        0.106           0.001           0.079   
                          (0.013)         (0.301)         (0.006)         (0.103)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.054           0.058           0.056           0.060   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s2.tex",                                           ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> mgroups("Treatment" "Multiple Treatment",                                       ///
> pattern(1 0 1 0) prefix(\multicolumn{@span}{c}{) suffix(}) span                 ///
> erepeat(\cmidrule(lr){@span}))                                                  ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of treatment allocation")
(output written to ./tables/table_s2.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s3 --------------------------------------
. 
. do "./scripts/script_table_s3.do"

. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov treatment akp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    1687.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9596
                                                Root MSE          =     3.8224

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   1.028215    .411632     2.50   0.015     .2090412    1.847389
         akp_jun |   1.084056   .0157499    68.83   0.000     1.052713    1.115399
recruitment_pool |   .0198922   .0151153     1.32   0.192    -.0101882    .0499726
           _cons |   4.589535   .8777807     5.23   0.000     2.842695    6.336374
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov treatment akp_jun recruitment_pool                          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     912.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9694
                                                Root MSE          =     3.3377

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.088677   .3651188     2.98   0.004     .3620676    1.815287
            akp_jun |    1.06079   .0155556    68.19   0.000     1.029833    1.091746
   recruitment_pool |   .0507082   .0157006     3.23   0.002      .019463    .0819534
treatment_nonterror |  -.5234295   .4052526    -1.29   0.200    -1.329908    .2830489
  casualty_district |  -.9695885   .9762579    -0.99   0.324    -2.912404    .9732266
   kurdish_district |   3.293495   .7638653     4.31   0.000     1.773355    4.813636
       akp_district |   2.075306   .3162353     6.56   0.000     1.445978    2.704634
   higher_education |  -.1195369   .0363287    -3.29   0.001    -.1918333   -.0472405
         min_margin |   .0170475   .0341554     0.50   0.619     -.050924     .085019
        turnout_jun |  -.1477943   .0517829    -2.85   0.005    -.2508456    -.044743
              _cons |   17.00327   4.681051     3.63   0.000     7.687679    26.31885
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov treatment treatment_multi akp_jun recruitment_pool,         ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1465.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9597
                                                Root MSE          =     3.8194

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   1.256686    .470101     2.67   0.009     .3211549    2.192216
 treatment_multi |  -1.471472   .7160207    -2.06   0.043    -2.896398    -.046545
         akp_jun |    1.08393   .0157353    68.89   0.000     1.052616    1.115244
recruitment_pool |   .0229247   .0158675     1.44   0.152    -.0086526     .054502
           _cons |   4.578108   .8749415     5.23   0.000     2.836919    6.319297
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov treatment treatment_multi akp_jun recruitment_pool          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     822.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3362

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.260685   .4073108     3.10   0.003     .4501104    2.071259
    treatment_multi |  -1.121329   .5095216    -2.20   0.031    -2.135309   -.1073488
            akp_jun |   1.060637   .0154344    68.72   0.000     1.029921    1.091352
   recruitment_pool |    .052969   .0165071     3.21   0.002     .0201189    .0858192
treatment_nonterror |  -.5018725    .396568    -1.27   0.209    -1.291068     .287323
  casualty_district |  -1.006431    .978949    -1.03   0.307    -2.954601    .9417399
   kurdish_district |   3.287623   .7553005     4.35   0.000     1.784528    4.790719
       akp_district |   2.068025   .3156711     6.55   0.000     1.439819     2.69623
   higher_education |  -.1186921   .0359752    -3.30   0.001     -.190285   -.0470993
         min_margin |    .017726   .0340341     0.52   0.604     -.050004    .0854561
        turnout_jun |  -.1486963   .0519929    -2.86   0.005    -.2521655   -.0452271
              _cons |   17.07216    4.69673     3.63   0.000     7.725375    26.41895
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S3: Main regression models, completing Table 1")

Table S3: Main regression models, completing Table 1
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   1.028*          1.089**         1.257**         1.261** 
                          (0.412)         (0.365)         (0.470)         (0.407)   

Multiple Treatment                                         -1.471*         -1.121*  
                                                          (0.716)         (0.510)   

Pre-test                    1.084***        1.061***        1.084***        1.061***
                          (0.016)         (0.016)         (0.016)         (0.015)   

Recruitment Pool            0.020           0.051**         0.023           0.053** 
                          (0.015)         (0.016)         (0.016)         (0.017)   

Non-terror Funeral                         -0.523                          -0.502   
                                          (0.405)                         (0.397)   

Attack District                            -0.970                          -1.006   
                                          (0.976)                         (0.979)   

Kurdish District                            3.293***                        3.288***
                                          (0.764)                         (0.755)   

AKP District                                2.075***                        2.068***
                                          (0.316)                         (0.316)   

Higher Education                           -0.120**                        -0.119** 
                                          (0.036)                         (0.036)   

Electoral Margin                            0.017                           0.018   
                                          (0.034)                         (0.034)   

Turnout                                    -0.148**                        -0.149** 
                                          (0.052)                         (0.052)   

Constant                    4.590***       17.003***        4.578***       17.072***
                          (0.878)         (4.681)         (0.875)         (4.697)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.960           0.969           0.960           0.970   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s3.tex",                                           ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Main regression models, completing Table 1")
(output written to ./tables/table_s3.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s5 --------------------------------------
. 
. do "./scripts/script_table_s5.do"

. * balance the treatment and control groups -------------------------------------
. 
. ebalance treatment recruitment_pool treatment_nonterror casualty_district       ///
> kurdish_district akp_district higher_education min_margin turnout_jun,          ///
> targets(1)


Data Setup
Treatment variable:   treatment
Covariate adjustment: recruitment_pool treatment_nonterror casualty_district kurdish_district akp_district higher_education min_margin turnout_jun 

Optimizing...
Iteration 1: Max Difference = 7240.12699
Iteration 2: Max Difference = 2661.23455
Iteration 3: Max Difference = 976.759948
Iteration 4: Max Difference = 357.092017
Iteration 5: Max Difference = 129.170745
Iteration 6: Max Difference = 45.43158
Iteration 7: Max Difference = 14.8810984
Iteration 8: Max Difference = 4.10205244
Iteration 9: Max Difference = .711016679
Iteration 10: Max Difference = .035421478
Iteration 11: Max Difference = .000102858
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 123     total of weights: 123
Control units: 847     total of weights: 123


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
recruitmen~l |     13.17      248.8      2.158 |      5.61      91.66      3.277 
treatment_~r |    .09756     .08876      2.713 |    .02834     .02756      5.685 
casualty_d~t |    .04065     .03932      4.652 |    .04959     .04718       4.15 
kurdish_di~t |     .1545      .1317      1.912 |     .1889      .1534       1.59 
akp_district |     .6504      .2292     -.6308 |     .6257      .2345     -.5197 
higher_edu~n |     8.423      26.03      2.099 |     7.103      17.64      2.262 
  min_margin |     4.272       21.2      1.777 |     4.812      27.35      2.072 
 turnout_jun |     85.63      14.25     -.7908 |     85.58      18.42      -.894 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
recruitmen~l |     13.17      248.8      2.158 |     13.17      334.2      1.637 
treatment_~r |    .09756     .08876      2.713 |    .09756     .08815      2.713 
casualty_d~t |    .04065     .03932      4.652 |    .04065     .03904      4.652 
kurdish_di~t |     .1545      .1317      1.912 |     .1545      .1308      1.912 
akp_district |     .6504      .2292     -.6308 |     .6504      .2276     -.6308 
higher_edu~n |     8.423      26.03      2.099 |     8.423      27.31      1.977 
  min_margin |     4.272       21.2      1.777 |     4.272      25.79      2.463 
 turnout_jun |     85.63      14.25     -.7908 |     85.63      15.67     -1.022 

. 
. * run the regressions models and save them to memory ---------------------------
. 
. svyset [pweight= _webal]

      pweight: _webal
          VCE: linearized
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>

. eststo: svy: reg akp_nov treatment akp_jun
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =        970
Number of PSUs     =       970                  Population size   =        246
                                                Design df         =        969
                                                F(   2,    968)   =    3231.75
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9506

------------------------------------------------------------------------------
             |             Linearized
     akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   treatment |   .9603925   .4173499     2.30   0.022     .1413788    1.779406
     akp_jun |   1.090227   .0135659    80.37   0.000     1.063605    1.116849
       _cons |   4.638567   .6238543     7.44   0.000     3.414306    5.862828
------------------------------------------------------------------------------
(est1 stored)

. eststo: svy: reg akp_nov treatment treatment_multi akp_jun
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =        970
Number of PSUs     =       970                  Population size   =        246
                                                Design df         =        969
                                                F(   3,    967)   =    2182.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9510

---------------------------------------------------------------------------------
                |             Linearized
        akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      treatment |   1.160232   .4781954     2.43   0.015      .221814     2.09865
treatment_multi |   -1.15351   .5855187    -1.97   0.049     -2.30254   -.0044789
        akp_jun |   1.088611   .0135523    80.33   0.000     1.062015    1.115206
          _cons |   4.709188   .6234918     7.55   0.000     3.485638    5.932738
---------------------------------------------------------------------------------
(est2 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N r2) wide label nodepvars nonotes nomtitles        ///
> collabels("Coefficient" "Std. Error" "Coefficient" "Std. Error")                ///
> order(treatment treatment_multi)                                                ///
> title("Table S5: Regression models based on entropy balancing")

Table S5: Regression models based on entropy balancing
------------------------------------------------------------------------------
                              (1)                          (2)                
                      Coefficient      Std. Error  Coefficient      Std. Error
------------------------------------------------------------------------------
Treatment                   0.960*        (0.417)        1.160*        (0.478)
Multiple Treatment                                      -1.154*        (0.586)
Pre-test                    1.090***      (0.014)        1.089***      (0.014)
Constant                    4.639***      (0.624)        4.709***      (0.623)
------------------------------------------------------------------------------
N                         970.000                      970.000                
r2                          0.951                        0.951                
------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s5.tex",                                           ///
> replace se b(3) se(3) stats(N r2) wide label nodepvars nonotes nomtitles        ///
> collabels("Coefficient" "Std. Error" "Coefficient" "Std. Error")                ///
> order(treatment treatment_multi)                                                ///
> title("Regression models based on entropy balancing")
(output written to ./tables/table_s5.tex)

. 
. * remove the entropy balancing weights -----------------------------------------
. 
. drop _webal

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s6 --------------------------------------
. 
. do "./scripts/script_table_s6.do"

. * generate the additional variable  --------------------------------------------
. 
. gen coffin = 0

. replace coffin = 1 if coffins == 1
(102 real changes made)

. 
. * label the additional variable ------------------------------------------------
. 
. label variable coffin              "Single Treatment"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov coffin akp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    1706.81
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9597
                                                Root MSE          =     3.8175

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
          coffin |   1.265512   .4704001     2.69   0.009      .329386    2.201638
         akp_jun |     1.0839   .0157502    68.82   0.000     1.052556    1.115244
recruitment_pool |   .0221986   .0151418     1.47   0.147    -.0079345    .0523317
           _cons |   4.578631   .8750394     5.23   0.000     2.837247    6.320015
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov coffin akp_jun recruitment_pool                             ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     896.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3345

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             coffin |   1.254667   .4044073     3.10   0.003      .449871    2.059463
            akp_jun |   1.060646   .0154154    68.80   0.000     1.029968    1.091324
   recruitment_pool |   .0534286   .0159364     3.35   0.001     .0217142     .085143
treatment_nonterror |  -.4949538   .3942168    -1.26   0.213     -1.27947    .2895625
  casualty_district |    -1.0122   .9768112    -1.04   0.303    -2.956116    .9317168
   kurdish_district |   3.286763   .7533289     4.36   0.000     1.787591    4.785935
       akp_district |    2.06683   .3158827     6.54   0.000     1.438203    2.695457
   higher_education |  -.1185996   .0360299    -3.29   0.001    -.1903014   -.0468978
         min_margin |   .0178253   .0339861     0.52   0.601    -.0498092    .0854597
        turnout_jun |  -.1488339   .0518954    -2.87   0.005     -.252109   -.0455587
              _cons |   17.08395   4.686475     3.65   0.000     7.757562    26.41033
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov coffin akp_jun recruitment_pool                             ///
> if treatment_multi == 0, cluster(province)

Linear regression                               Number of obs     =        949
                                                F(3, 80)          =    1613.16
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9594
                                                Root MSE          =     3.8486

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
          coffin |   1.237255   .4728056     2.62   0.011     .2963414    2.178168
         akp_jun |   1.083573   .0158456    68.38   0.000     1.052039    1.115107
recruitment_pool |   .0266924   .0175281     1.52   0.132    -.0081896    .0615745
           _cons |   4.572764   .8802656     5.19   0.000     2.820979    6.324548
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov coffin akp_jun recruitment_pool                             ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun if treatment_multi == 0,                ///
> cluster(province)

Linear regression                               Number of obs     =        949
                                                F(10, 80)         =     854.37
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9692
                                                Root MSE          =     3.3628

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             coffin |   1.235646   .4116825     3.00   0.004     .4163719     2.05492
            akp_jun |   1.061042   .0156561    67.77   0.000     1.029886    1.092199
   recruitment_pool |   .0559569   .0182409     3.07   0.003     .0196564    .0922574
treatment_nonterror |  -.3775151   .4174258    -0.90   0.369    -1.208219    .4531887
  casualty_district |  -1.045133     .98277    -1.06   0.291    -3.000907    .9106417
   kurdish_district |   3.331766   .7553737     4.41   0.000     1.828524    4.835007
       akp_district |   2.094489   .3252165     6.44   0.000     1.447288    2.741691
   higher_education |  -.1182458   .0373923    -3.16   0.002    -.1926588   -.0438328
         min_margin |   .0171202    .034199     0.50   0.618    -.0509381    .0851785
        turnout_jun |  -.1477848   .0520901    -2.84   0.006    -.2514474   -.0441222
              _cons |   16.93271   4.709037     3.60   0.001     7.561426    26.30399
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> mgroups("All Districts Included" "Excluding the Multiply-Treated",              ///
> pattern(1 0 1 0) span)                                                          ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S6: Regression models with single treatment")

Table S6: Regression models with single treatment
------------------------------------------------------------------------------------
                     All Districts Included          Excluding the Multiply-Treated 
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Single Treatment            1.266**         1.255**         1.237*          1.236** 
                          (0.470)         (0.404)         (0.473)         (0.412)   

Pre-test                    1.084***        1.061***        1.084***        1.061***
                          (0.016)         (0.015)         (0.016)         (0.016)   

Recruitment Pool            0.022           0.053**         0.027           0.056** 
                          (0.015)         (0.016)         (0.018)         (0.018)   

Non-terror Funeral                         -0.495                          -0.378   
                                          (0.394)                         (0.417)   

Attack District                            -1.012                          -1.045   
                                          (0.977)                         (0.983)   

Kurdish District                            3.287***                        3.332***
                                          (0.753)                         (0.755)   

AKP District                                2.067***                        2.094***
                                          (0.316)                         (0.325)   

Higher Education                           -0.119**                        -0.118** 
                                          (0.036)                         (0.037)   

Electoral Margin                            0.018                           0.017   
                                          (0.034)                         (0.034)   

Turnout                                    -0.149**                        -0.148** 
                                          (0.052)                         (0.052)   

Constant                    4.579***       17.084***        4.573***       16.933***
                          (0.875)         (4.686)         (0.880)         (4.709)   
------------------------------------------------------------------------------------
N                         970.000         970.000         949.000         949.000   
Clusters                       81         81         81         81   
r2                          0.960           0.970           0.959           0.969   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s6.tex",                                           ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> mgroups("All Districts Included" "Excluding the Multiply-Treated",              ///
> pattern(1 0 1 0) prefix(\multicolumn{@span}{c}{) suffix(}) span                 ///
> erepeat(\cmidrule(lr){@span}))                                                  ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models with single treatment")
(output written to ./tables/table_s6.tex)

. 
. * remove the additional variable -----------------------------------------------
. 
. drop coffin

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s7 --------------------------------------
. 
. do "./scripts/script_table_s7.do"

. * generate the additional variable  --------------------------------------------
. 
. gen coffins_squared = coffins * coffins

. 
. * label the additional variables -----------------------------------------------
. 
. label variable coffins             "Treatments"

. label variable coffins_squared     "Treatments Squared"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov coffins akp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    1682.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9594
                                                Root MSE          =     3.8314

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         coffins |   .4594644   .2604007     1.76   0.081    -.0587494    .9776782
         akp_jun |   1.084709   .0157599    68.83   0.000     1.053346    1.116073
recruitment_pool |   .0220094   .0160297     1.37   0.174    -.0098907    .0539095
           _cons |   4.604537   .8798606     5.23   0.000     2.853558    6.355515
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov coffins akp_jun recruitment_pool                            ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     904.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9693
                                                Root MSE          =     3.3467

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
            coffins |   .5700032   .2289835     2.49   0.015     .1143116    1.025695
            akp_jun |   1.061303   .0156568    67.79   0.000     1.030145    1.092461
   recruitment_pool |   .0521145   .0165173     3.16   0.002     .0192439     .084985
treatment_nonterror |  -.5033172   .4098973    -1.23   0.223    -1.319039    .3124045
  casualty_district |  -.9609029   .9766176    -0.98   0.328    -2.904434    .9826281
   kurdish_district |   3.293175   .7729967     4.26   0.000     1.754863    4.831487
       akp_district |   2.077793   .3171539     6.55   0.000     1.446637     2.70895
   higher_education |  -.1209657   .0365409    -3.31   0.001    -.1936844   -.0482469
         min_margin |   .0168542   .0341155     0.49   0.623    -.0510378    .0847463
        turnout_jun |   -.147727   .0517959    -2.85   0.006    -.2508041   -.0446499
              _cons |   17.02227   4.682555     3.64   0.000     7.703687    26.34085
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov coffins coffins_squared akp_jun recruitment_pool,           ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1337.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9597
                                                Root MSE          =     3.8231

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         coffins |   1.638735   .6499835     2.52   0.014     .3452269    2.932244
 coffins_squared |  -.5827452   .2822123    -2.06   0.042    -1.144365   -.0211248
         akp_jun |   1.083974   .0157894    68.65   0.000     1.052552    1.115396
recruitment_pool |    .021834    .015592     1.40   0.165    -.0091952    .0528631
           _cons |   4.592295   .8782148     5.23   0.000     2.844592    6.339998
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov coffins coffins_squared akp_jun recruitment_pool            ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     843.65
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9694
                                                Root MSE          =     3.3407

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
            coffins |   1.522048   .5438038     2.80   0.006     .4398436    2.604252
    coffins_squared |  -.4720623   .2150828    -2.19   0.031    -.9000907   -.0440339
            akp_jun |   1.060854   .0155669    68.15   0.000     1.029875    1.091833
   recruitment_pool |   .0515324   .0161517     3.19   0.002     .0193894    .0836754
treatment_nonterror |    -.46476   .3963419    -1.17   0.244    -1.253506    .3239855
  casualty_district |  -.9862085   .9780746    -1.01   0.316    -2.932639     .960222
   kurdish_district |   3.294405   .7611562     4.33   0.000     1.779656    4.809154
       akp_district |   2.071173   .3162712     6.55   0.000     1.441773    2.700573
   higher_education |  -.1181641   .0359603    -3.29   0.002    -.1897275   -.0466007
         min_margin |   .0174536   .0342056     0.51   0.611    -.0506177    .0855249
        turnout_jun |  -.1479314   .0518306    -2.85   0.005    -.2510776   -.0447851
              _cons |   17.00725   4.683349     3.63   0.000     7.687091    26.32742
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(coffins coffins_squared)                                                  ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S7: Regression models with quadratic treatment")

Table S7: Regression models with quadratic treatment
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatments                  0.459           0.570*          1.639*          1.522** 
                          (0.260)         (0.229)         (0.650)         (0.544)   

Treatments Squared                                         -0.583*         -0.472*  
                                                          (0.282)         (0.215)   

Pre-test                    1.085***        1.061***        1.084***        1.061***
                          (0.016)         (0.016)         (0.016)         (0.016)   

Recruitment Pool            0.022           0.052**         0.022           0.052** 
                          (0.016)         (0.017)         (0.016)         (0.016)   

Non-terror Funeral                         -0.503                          -0.465   
                                          (0.410)                         (0.396)   

Attack District                            -0.961                          -0.986   
                                          (0.977)                         (0.978)   

Kurdish District                            3.293***                        3.294***
                                          (0.773)                         (0.761)   

AKP District                                2.078***                        2.071***
                                          (0.317)                         (0.316)   

Higher Education                           -0.121**                        -0.118** 
                                          (0.037)                         (0.036)   

Electoral Margin                            0.017                           0.017   
                                          (0.034)                         (0.034)   

Turnout                                    -0.148**                        -0.148** 
                                          (0.052)                         (0.052)   

Constant                    4.605***       17.022***        4.592***       17.007***
                          (0.880)         (4.683)         (0.878)         (4.683)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.959           0.969           0.960           0.969   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s7.tex",                                           ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(coffins coffins_squared)                                                  ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models with quadratic treatment")
(output written to ./tables/table_s7.tex)

. 
. * drop the additional variable -------------------------------------------------
. 
. drop coffins_squared

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s8 --------------------------------------
. 
. do "./scripts/script_table_s8.do"

. * recode the missing values as 0 for untreated districts -----------------------
. 
. replace mean_days = 0 if mean_days == .
(847 real changes made)

. replace min_days = 0 if min_days == .
(847 real changes made)

. 
. * label the additional variables -----------------------------------------------
. 
. label variable mean_days           "Timing (Mean)"

. label variable min_days            "Timing (Minimum)"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov treatment mean_days akp_jun recruitment_pool,               ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1284.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9596
                                                Root MSE          =      3.824

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .6712891   .7277779     0.92   0.359    -.7770351    2.119613
       mean_days |   .0060034   .0120949     0.50   0.621    -.0180661     .030073
         akp_jun |   1.084053   .0157525    68.82   0.000     1.052705    1.115401
recruitment_pool |   .0199812   .0150804     1.32   0.189    -.0100296    .0499921
           _cons |   4.589172   .8777734     5.23   0.000     2.842348    6.335997
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov treatment mean_days akp_jun recruitment_pool                ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     879.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3379

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.828102   .5412521     3.38   0.001     .7509764    2.905228
          mean_days |  -.0124651   .0086462    -1.44   0.153    -.0296715    .0047413
            akp_jun |   1.060596   .0155784    68.08   0.000     1.029594    1.091598
   recruitment_pool |   .0505813   .0156822     3.23   0.002     .0193728    .0817899
treatment_nonterror |  -.4813172   .4077214    -1.18   0.241    -1.292709    .3300742
  casualty_district |  -.9610394   .9774373    -0.98   0.328    -2.906202    .9841227
   kurdish_district |   3.303435   .7648296     4.32   0.000     1.781375    4.825494
       akp_district |   2.091224   .3149059     6.64   0.000     1.464542    2.717907
   higher_education |  -.1206343   .0361108    -3.34   0.001    -.1924971   -.0487715
         min_margin |    .016968   .0341017     0.50   0.620    -.0508965    .0848325
        turnout_jun |  -.1478215   .0517516    -2.86   0.005    -.2508106   -.0448325
              _cons |   17.00961   4.681058     3.63   0.000     7.694009    26.32521
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov treatment min_days akp_jun recruitment_pool,                ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1264.49
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9597
                                                Root MSE          =     3.8232

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .3874005   .6792629     0.57   0.570    -.9643757    1.739177
        min_days |   .0112757   .0122428     0.92   0.360    -.0130883    .0356397
         akp_jun |   1.084029    .015747    68.84   0.000     1.052691    1.115367
recruitment_pool |    .020373   .0150604     1.35   0.180    -.0095982    .0503441
           _cons |   4.588036   .8771968     5.23   0.000     2.842359    6.333714
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov treatment min_days akp_jun recruitment_pool                 ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     860.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =      3.339

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.473612   .4925042     2.99   0.004     .4934971    2.453726
           min_days |   -.006778   .0086436    -0.78   0.435    -.0239793    .0104232
            akp_jun |   1.060696   .0155763    68.10   0.000     1.029698    1.091693
   recruitment_pool |   .0505015   .0157165     3.21   0.002     .0192246    .0817783
treatment_nonterror |  -.5106438   .4084766    -1.25   0.215    -1.323538    .3022506
  casualty_district |  -.9603913   .9783654    -0.98   0.329    -2.907401    .9866179
   kurdish_district |   3.297864   .7660962     4.30   0.000     1.773284    4.822445
       akp_district |   2.083962   .3142245     6.63   0.000     1.458635    2.709288
   higher_education |  -.1204247   .0359844    -3.35   0.001     -.192036   -.0488133
         min_margin |   .0168803    .034166     0.49   0.623    -.0511122    .0848728
        turnout_jun |  -.1477159   .0517893    -2.85   0.006    -.2507799    -.044652
              _cons |   17.00194   4.681711     3.63   0.000     7.685035    26.31884
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment mean_days min_days)                                             ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S8: Regression models with treatment timing")

Table S8: Regression models with treatment timing
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   0.671           1.828**         0.387           1.474** 
                          (0.728)         (0.541)         (0.679)         (0.493)   

Timing (Mean)               0.006          -0.012                                   
                          (0.012)         (0.009)                                   

Timing (Minimum)                                            0.011          -0.007   
                                                          (0.012)         (0.009)   

Pre-test                    1.084***        1.061***        1.084***        1.061***
                          (0.016)         (0.016)         (0.016)         (0.016)   

Recruitment Pool            0.020           0.051**         0.020           0.051** 
                          (0.015)         (0.016)         (0.015)         (0.016)   

Non-terror Funeral                         -0.481                          -0.511   
                                          (0.408)                         (0.408)   

Attack District                            -0.961                          -0.960   
                                          (0.977)                         (0.978)   

Kurdish District                            3.303***                        3.298***
                                          (0.765)                         (0.766)   

AKP District                                2.091***                        2.084***
                                          (0.315)                         (0.314)   

Higher Education                           -0.121**                        -0.120** 
                                          (0.036)                         (0.036)   

Electoral Margin                            0.017                           0.017   
                                          (0.034)                         (0.034)   

Turnout                                    -0.148**                        -0.148** 
                                          (0.052)                         (0.052)   

Constant                    4.589***       17.010***        4.588***       17.002***
                          (0.878)         (4.681)         (0.877)         (4.682)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.960           0.969           0.960           0.969   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s8.tex",                                           ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment mean_days min_days)                                             ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models with treatment timing")
(output written to ./tables/table_s8.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s9 --------------------------------------
. 
. do "./scripts/script_table_s9.do"

. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov treatment akp_jun recruitment_pool                          ///
> if coffins == coffins_pkk, cluster(province)

Linear regression                               Number of obs     =        965
                                                F(3, 80)          =    1686.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9607
                                                Root MSE          =     3.7722

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .8331667   .3897045     2.14   0.036       .05763    1.608703
         akp_jun |   1.083075   .0156604    69.16   0.000      1.05191    1.114241
recruitment_pool |   .0207403   .0154823     1.34   0.184    -.0100704     .051551
           _cons |   4.628151   .8776363     5.27   0.000     2.881599    6.374703
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov treatment akp_jun recruitment_pool                          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun if coffins == coffins_pkk,              ///
> cluster(province)

Linear regression                               Number of obs     =        965
                                                F(10, 80)         =     942.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9700
                                                Root MSE          =     3.3095

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   .9084509   .3302236     2.75   0.007      .251285    1.565617
            akp_jun |   1.060812   .0154888    68.49   0.000     1.029988    1.091635
   recruitment_pool |   .0507407   .0156705     3.24   0.002     .0195555     .081926
treatment_nonterror |  -.4708204   .3935792    -1.20   0.235    -1.254068    .3124272
  casualty_district |  -.9196703   .9707715    -0.95   0.346    -2.851567    1.012227
   kurdish_district |   3.216976   .7221107     4.45   0.000      1.77993    4.654022
       akp_district |   2.048257   .3143941     6.51   0.000     1.422593    2.673922
   higher_education |  -.1157019   .0353762    -3.27   0.002    -.1861027    -.045301
         min_margin |   .0200281   .0336222     0.60   0.553    -.0468822    .0869384
        turnout_jun |  -.1410087   .0517202    -2.73   0.008    -.2439353   -.0380822
              _cons |   16.40724   4.677373     3.51   0.001     7.098973    25.71551
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov treatment treatment_multi akp_jun recruitment_pool          ///
> if coffins == coffins_pkk, cluster(province)

Linear regression                               Number of obs     =        965
                                                F(4, 80)          =    1498.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9608
                                                Root MSE          =     3.7705

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   1.037955    .426404     2.43   0.017     .1893839    1.886526
 treatment_multi |  -1.261602   .6316697    -2.00   0.049    -2.518664   -.0045391
         akp_jun |   1.082954   .0156513    69.19   0.000     1.051807    1.114101
recruitment_pool |   .0232681   .0160254     1.45   0.150    -.0086235    .0551598
           _cons |   4.619347    .874933     5.28   0.000     2.878174    6.360519
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov treatment treatment_multi akp_jun recruitment_pool          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun if coffins == coffins_pkk,              ///
> cluster(province)

Linear regression                               Number of obs     =        965
                                                F(11, 80)         =     855.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9700
                                                Root MSE          =     3.3089

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.058948   .3623748     2.92   0.005     .3377988    1.780096
    treatment_multi |  -.9377385   .4664716    -2.01   0.048    -1.866047   -.0094303
            akp_jun |   1.060658   .0153901    68.92   0.000      1.03003    1.091285
   recruitment_pool |   .0525887   .0163296     3.22   0.002     .0200917    .0850858
treatment_nonterror |  -.4544445   .3872978    -1.17   0.244    -1.225192    .3163027
  casualty_district |   -.950906   .9727804    -0.98   0.331    -2.886801    .9849887
   kurdish_district |   3.211776   .7155674     4.49   0.000     1.787751    4.635801
       akp_district |   2.042232   .3140364     6.50   0.000      1.41728    2.667185
   higher_education |   -.115074    .035116    -3.28   0.002    -.1849571   -.0451908
         min_margin |   .0204858   .0335389     0.61   0.543    -.0462588    .0872304
        turnout_jun |  -.1418524   .0519132    -2.73   0.008    -.2451629   -.0385418
              _cons |   16.47505   4.692458     3.51   0.001     7.136762    25.81334
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S9: Regression models with PKK-inflicted treatment only")

Table S9: Regression models with PKK-inflicted treatment only
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   0.833*          0.908**         1.038*          1.059** 
                          (0.390)         (0.330)         (0.426)         (0.362)   

Multiple Treatment                                         -1.262*         -0.938*  
                                                          (0.632)         (0.466)   

Pre-test                    1.083***        1.061***        1.083***        1.061***
                          (0.016)         (0.015)         (0.016)         (0.015)   

Recruitment Pool            0.021           0.051**         0.023           0.053** 
                          (0.015)         (0.016)         (0.016)         (0.016)   

Non-terror Funeral                         -0.471                          -0.454   
                                          (0.394)                         (0.387)   

Attack District                            -0.920                          -0.951   
                                          (0.971)                         (0.973)   

Kurdish District                            3.217***                        3.212***
                                          (0.722)                         (0.716)   

AKP District                                2.048***                        2.042***
                                          (0.314)                         (0.314)   

Higher Education                           -0.116**                        -0.115** 
                                          (0.035)                         (0.035)   

Electoral Margin                            0.020                           0.020   
                                          (0.034)                         (0.034)   

Turnout                                    -0.141**                        -0.142** 
                                          (0.052)                         (0.052)   

Constant                    4.628***       16.407***        4.619***       16.475***
                          (0.878)         (4.677)         (0.875)         (4.692)   
------------------------------------------------------------------------------------
N                         965.000         965.000         965.000         965.000   
Clusters                       81         81         81         81   
r2                          0.961           0.970           0.961           0.970   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s9.tex",                                           ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models with PKK-inflicted treatment only")
(output written to ./tables/table_s9.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s10 -------------------------------------
. 
. do "./scripts/script_table_s10.do"

. * relabel the turnout in june as pre-test --------------------------------------
. label variable turnout_jun         "Pre-test"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg turnout_nov treatment turnout_jun recruitment_pool,                 ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =     160.19
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7064
                                                Root MSE          =     2.3598

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
     turnout_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .5053262   .2487349     2.03   0.046      .010328    1.000324
     turnout_jun |   .8527816   .0392439    21.73   0.000     .7746837    .9308796
recruitment_pool |   .0250858   .0083053     3.02   0.003     .0085577    .0416138
           _cons |   12.79104   3.437072     3.72   0.000     5.951044    19.63103
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg turnout_nov treatment turnout_jun recruitment_pool                  ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(9, 80)          =     118.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7806
                                                Root MSE          =     2.0463

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
        turnout_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   .3629662   .2196739     1.65   0.102    -.0741988    .8001312
        turnout_jun |   .8329278   .0300466    27.72   0.000     .7731331    .8927224
   recruitment_pool |   .0267912   .0082254     3.26   0.002     .0104222    .0431602
treatment_nonterror |   .3350216   .2473695     1.35   0.179    -.1572595    .8273026
  casualty_district |   -.472746   .4388942    -1.08   0.285    -1.346173    .4006814
   kurdish_district |  -2.169761   .6306844    -3.44   0.001    -3.424863   -.9146589
       akp_district |   1.167783   .1965966     5.94   0.000     .7765429    1.559022
   higher_education |  -.0274891   .0314194    -0.87   0.384    -.0900156    .0350375
         min_margin |  -.0662273   .0214627    -3.09   0.003    -.1089395   -.0235152
              _cons |   14.68762   2.631719     5.58   0.000     9.450329    19.92491
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg turnout_nov treatment treatment_multi turnout_jun recruitment_pool, ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =     120.02
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7064
                                                Root MSE          =     2.3609

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
     turnout_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .5305292   .2686356     1.97   0.052    -.0040728    1.065131
 treatment_multi |  -.1627606   .3544849    -0.46   0.647     -.868208    .5426869
     turnout_jun |   .8527062   .0392816    21.71   0.000     .7745334     .930879
recruitment_pool |   .0254284   .0085006     2.99   0.004     .0085116    .0423452
           _cons |   12.79557   3.440177     3.72   0.000     5.949396    19.64174
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg turnout_nov treatment treatment_multi turnout_jun recruitment_pool  ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     107.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7806
                                                Root MSE          =     2.0472

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
        turnout_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |    .393528   .2480138     1.59   0.117    -.1000352    .8870911
    treatment_multi |  -.1995118   .3556947    -0.56   0.576    -.9073667    .5083431
        turnout_jun |   .8327916    .030087    27.68   0.000     .7729165    .8926667
   recruitment_pool |   .0271951   .0084493     3.22   0.002     .0103804    .0440097
treatment_nonterror |   .3387586   .2453987     1.38   0.171    -.1496005    .8271176
  casualty_district |  -.4790992   .4364366    -1.10   0.276    -1.347636    .3894372
   kurdish_district |  -2.170422   .6316908    -3.44   0.001    -3.427527   -.9133172
       akp_district |    1.16608   .1970461     5.92   0.000     .7739462    1.558215
   higher_education |   -.027316    .031364    -0.87   0.386    -.0897323    .0351003
         min_margin |  -.0661013   .0214781    -3.08   0.003     -.108844   -.0233585
              _cons |   14.69657   2.634879     5.58   0.000     9.452995    19.94015
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S10: Regression models of electoral turnout")

Table S10: Regression models of electoral turnout
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   0.505*          0.363           0.531           0.394   
                          (0.249)         (0.220)         (0.269)         (0.248)   

Multiple Treatment                                         -0.163          -0.200   
                                                          (0.354)         (0.356)   

Pre-test                    0.853***        0.833***        0.853***        0.833***
                          (0.039)         (0.030)         (0.039)         (0.030)   

Recruitment Pool            0.025**         0.027**         0.025**         0.027** 
                          (0.008)         (0.008)         (0.009)         (0.008)   

Non-terror Funeral                          0.335                           0.339   
                                          (0.247)                         (0.245)   

Attack District                            -0.473                          -0.479   
                                          (0.439)                         (0.436)   

Kurdish District                           -2.170***                       -2.170***
                                          (0.631)                         (0.632)   

AKP District                                1.168***                        1.166***
                                          (0.197)                         (0.197)   

Higher Education                           -0.027                          -0.027   
                                          (0.031)                         (0.031)   

Electoral Margin                           -0.066**                        -0.066** 
                                          (0.021)                         (0.021)   

Constant                   12.791***       14.688***       12.796***       14.697***
                          (3.437)         (2.632)         (3.440)         (2.635)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.706           0.781           0.706           0.781   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s10.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of electoral turnout")
(output written to ./tables/table_s10.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
. * relabel turnout back to original ---------------------------------------------
. label variable turnout_jun         "Turnout"

. 
end of do-file

. 
. * source the script that creates table s11 -------------------------------------
. 
. do "./scripts/script_table_s11.do"

. * define interaction terms -----------------------------------------------------
. 
. gen single_pt = treatment * akp_jun

. gen multi_pt = treatment_multi * akp_jun

. 
. * label the terms --------------------------------------------------------------
. 
. label variable single_pt           "Treatment X Pre-test"

. label variable multi_pt            "Multiple Treatment X Pre-test"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov treatment single_pt akp_jun recruitment_pool,               ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1266.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9596
                                                Root MSE          =     3.8243

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .7738546   1.674686     0.46   0.645    -2.558877    4.106586
       single_pt |   .0055881   .0352842     0.16   0.875    -.0646296    .0758059
         akp_jun |    1.08354   .0161012    67.30   0.000     1.051498    1.115582
recruitment_pool |   .0199997   .0152409     1.31   0.193    -.0103306      .05033
           _cons |   4.611755   .8984332     5.13   0.000     2.823816    6.399694
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov treatment single_pt akp_jun recruitment_pool                ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     873.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3377

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   2.096197   1.374216     1.53   0.131    -.6385791    4.830973
          single_pt |  -.0220844    .028979    -0.76   0.448    -.0797545    .0355857
            akp_jun |    1.06302   .0165635    64.18   0.000     1.030058    1.095983
   recruitment_pool |   .0503944   .0156263     3.22   0.002     .0192971    .0814917
treatment_nonterror |  -.5637876   .3955277    -1.43   0.158    -1.350913    .2233377
  casualty_district |  -.9967529   .9756247    -1.02   0.310    -2.938308    .9448021
   kurdish_district |   3.330982   .7572896     4.40   0.000     1.823927    4.838036
       akp_district |   2.085422    .314935     6.62   0.000     1.458682    2.712163
   higher_education |  -.1191365   .0362112    -3.29   0.001     -.191199    -.047074
         min_margin |   .0177714   .0343942     0.52   0.607    -.0506753     .086218
        turnout_jun |  -.1459578    .051922    -2.81   0.006    -.2492859   -.0426297
              _cons |   16.73196   4.719594     3.55   0.001     7.339665    26.12425
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov treatment treatment_multi multi_pt akp_jun                  ///
> recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(5, 80)          =    1308.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9598
                                                Root MSE          =     3.8199

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |    1.25552   .4710287     2.67   0.009     .3181435    2.192897
 treatment_multi |  -3.791396   1.847763    -2.05   0.043    -7.468562   -.1142308
        multi_pt |   .0555785   .0402464     1.38   0.171    -.0245144    .1356715
         akp_jun |   1.083272   .0158886    68.18   0.000     1.051653    1.114892
recruitment_pool |   .0233958   .0157993     1.48   0.143    -.0080459    .0548375
           _cons |   4.604543   .8816647     5.22   0.000     2.849974    6.359111
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov treatment treatment_multi multi_pt akp_jun recruitment_pool ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(12, 80)         =    1046.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3377

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.261791   .4074164     3.10   0.003     .4510069    2.072576
    treatment_multi |  -.2320244   1.009804    -0.23   0.819    -2.241598    1.777549
           multi_pt |  -.0212584   .0221214    -0.96   0.339    -.0652815    .0227646
            akp_jun |   1.061001   .0155032    68.44   0.000     1.030149    1.091854
   recruitment_pool |   .0528932   .0164712     3.21   0.002     .0201144     .085672
treatment_nonterror |  -.5171686   .3989585    -1.30   0.199    -1.311121    .2767842
  casualty_district |  -1.005627    .978814    -1.03   0.307    -2.953529     .942275
   kurdish_district |   3.299364   .7543485     4.37   0.000     1.798163    4.800566
       akp_district |   2.067955   .3154065     6.56   0.000     1.440276    2.695634
   higher_education |  -.1190087   .0360443    -3.30   0.001    -.1907392   -.0472782
         min_margin |   .0178323   .0340046     0.52   0.601    -.0498389    .0855036
        turnout_jun |  -.1482308    .051976    -2.85   0.006    -.2516662   -.0447953
              _cons |   17.01657   4.695488     3.62   0.001     7.672255    26.36089
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi single_pt multi_pt)                             ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S11: Regression models with interaction terms, constructed with Pre-test")

Table S11: Regression models with interaction terms, constructed with Pre-test
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   0.774           2.096           1.256**         1.262** 
                          (1.675)         (1.374)         (0.471)         (0.407)   

Multiple Treatment                                         -3.791*         -0.232   
                                                          (1.848)         (1.010)   

Treatment X Pre-test        0.006          -0.022                                   
                          (0.035)         (0.029)                                   

Multiple Treatment~t                                        0.056          -0.021   
                                                          (0.040)         (0.022)   

Pre-test                    1.084***        1.063***        1.083***        1.061***
                          (0.016)         (0.017)         (0.016)         (0.016)   

Recruitment Pool            0.020           0.050**         0.023           0.053** 
                          (0.015)         (0.016)         (0.016)         (0.016)   

Non-terror Funeral                         -0.564                          -0.517   
                                          (0.396)                         (0.399)   

Attack District                            -0.997                          -1.006   
                                          (0.976)                         (0.979)   

Kurdish District                            3.331***                        3.299***
                                          (0.757)                         (0.754)   

AKP District                                2.085***                        2.068***
                                          (0.315)                         (0.315)   

Higher Education                           -0.119**                        -0.119** 
                                          (0.036)                         (0.036)   

Electoral Margin                            0.018                           0.018   
                                          (0.034)                         (0.034)   

Turnout                                    -0.146**                        -0.148** 
                                          (0.052)                         (0.052)   

Constant                    4.612***       16.732***        4.605***       17.017***
                          (0.898)         (4.720)         (0.882)         (4.695)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.960           0.969           0.960           0.970   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s11.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi single_pt multi_pt)                             ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models with interaction terms, constructed with \textit{Pre-test}")
(output written to ./tables/table_s11.tex)

. 
. * remove the interaction variables ---------------------------------------------
. 
. drop single_pt multi_pt

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s12 -------------------------------------
. 
. do "./scripts/script_table_s12.do"

. * define interaction terms -----------------------------------------------------
. 
. gen single_ad = treatment * akp_district

. gen multi_ad = treatment_multi * akp_district

. 
. * label the terms --------------------------------------------------------------
. 
. label variable single_ad           "Treatment X AKP District"

. label variable multi_ad            "Multiple Treatment X AKP District"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg akp_nov treatment single_ad akp_jun recruitment_pool akp_district,  ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(5, 80)          =    1244.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9620
                                                Root MSE          =     3.7151

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .6439083    .557071     1.16   0.251    -.4646983    1.752515
       single_ad |    .653732    .796995     0.82   0.415    -.9323386    2.239803
         akp_jun |   1.049318   .0168128    62.41   0.000      1.01586    1.082777
recruitment_pool |   .0132152   .0153753     0.86   0.393    -.0173827     .043813
    akp_district |   2.158954   .3862118     5.59   0.000     1.390368     2.92754
           _cons |   4.812536   .8458667     5.69   0.000     3.129208    6.495865
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg akp_nov treatment single_ad akp_jun recruitment_pool                ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     832.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9694
                                                Root MSE          =     3.3394

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.157339   .5247713     2.21   0.030     .1130109    2.201667
          single_ad |  -.1057267   .6990651    -0.15   0.880    -1.496911    1.285457
            akp_jun |   1.060781   .0155525    68.21   0.000     1.029831    1.091731
   recruitment_pool |   .0507288   .0156807     3.24   0.002     .0195233    .0819343
treatment_nonterror |  -.5263979    .400408    -1.31   0.192    -1.323235    .2704394
  casualty_district |  -.9728548   .9758218    -1.00   0.322    -2.914802    .9690923
   kurdish_district |   3.296966   .7616778     4.33   0.000     1.781179    4.812753
       akp_district |   2.088701   .3238835     6.45   0.000     1.444152    2.733249
   higher_education |  -.1195545   .0362812    -3.30   0.001    -.1917565   -.0473526
         min_margin |   .0171394   .0342593     0.50   0.618    -.0510389    .0853177
        turnout_jun |  -.1478439   .0518367    -2.85   0.006    -.2510022   -.0446856
              _cons |   16.99868   4.683987     3.63   0.000      7.67725    26.32011
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg akp_nov treatment treatment_multi multi_ad akp_jun  akp_district    ///
> recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(6, 80)          =    1158.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9620
                                                Root MSE          =     3.7143

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   1.274418    .439701     2.90   0.005     .3993846    2.149451
 treatment_multi |  -1.295594   .7641097    -1.70   0.094    -2.816221    .2250325
        multi_ad |  -.0639452   .7745816    -0.08   0.934    -1.605412    1.477521
         akp_jun |   1.049163    .016766    62.58   0.000     1.015798    1.082528
    akp_district |   2.235748   .3948467     5.66   0.000     1.449978    3.021518
recruitment_pool |   .0159177     .01624     0.98   0.330    -.0164011    .0482364
           _cons |   4.756201   .8380845     5.68   0.000      3.08836    6.424043
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg akp_nov treatment treatment_multi multi_ad akp_jun recruitment_pool ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(12, 80)         =     765.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9695
                                                Root MSE          =     3.3366

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            akp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   1.259271   .4069285     3.09   0.003     .4494576    2.069084
    treatment_multi |  -.3829191   .5053816    -0.76   0.451     -1.38866    .6228223
           multi_ad |   -1.28666    .640766    -2.01   0.048    -2.561825   -.0114956
            akp_jun |   1.060384   .0154345    68.70   0.000     1.029668    1.091099
   recruitment_pool |    .053223   .0165759     3.21   0.002     .0202359    .0862101
treatment_nonterror |  -.4928468   .3858439    -1.28   0.205    -1.260701    .2750071
  casualty_district |   -1.01088   .9787625    -1.03   0.305     -2.95868    .9369195
   kurdish_district |   3.294911   .7515337     4.38   0.000     1.799311     4.79051
       akp_district |   2.101029   .3249029     6.47   0.000     1.454452    2.747607
   higher_education |  -.1200192   .0360638    -3.33   0.001    -.1917884     -.04825
         min_margin |     .01811   .0339578     0.53   0.595    -.0494682    .0856882
        turnout_jun |  -.1486519   .0520887    -2.85   0.005    -.2523119    -.044992
              _cons |   17.06366   4.702919     3.63   0.001     7.704551    26.42277
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi single_ad multi_ad akp_jun recruitment_pool     ///
> treatment_nonterror casualty_district kurdish_district akp_district)            ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S12: Regression models with interaction terms, constructed with AKP District")

Table S12: Regression models with interaction terms, constructed with AKP District
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   0.644           1.157*          1.274**         1.259** 
                          (0.557)         (0.525)         (0.440)         (0.407)   

Multiple Treatment                                         -1.296          -0.383   
                                                          (0.764)         (0.505)   

Treatment X AKP Di~t        0.654          -0.106                                   
                          (0.797)         (0.699)                                   

Multiple Treatment~c                                       -0.064          -1.287*  
                                                          (0.775)         (0.641)   

Pre-test                    1.049***        1.061***        1.049***        1.060***
                          (0.017)         (0.016)         (0.017)         (0.015)   

Recruitment Pool            0.013           0.051**         0.016           0.053** 
                          (0.015)         (0.016)         (0.016)         (0.017)   

Non-terror Funeral                         -0.526                          -0.493   
                                          (0.400)                         (0.386)   

Attack District                            -0.973                          -1.011   
                                          (0.976)                         (0.979)   

Kurdish District                            3.297***                        3.295***
                                          (0.762)                         (0.752)   

AKP District                2.159***        2.089***        2.236***        2.101***
                          (0.386)         (0.324)         (0.395)         (0.325)   

Higher Education                           -0.120**                        -0.120** 
                                          (0.036)                         (0.036)   

Electoral Margin                            0.017                           0.018   
                                          (0.034)                         (0.034)   

Turnout                                    -0.148**                        -0.149** 
                                          (0.052)                         (0.052)   

Constant                    4.813***       16.999***        4.756***       17.064***
                          (0.846)         (4.684)         (0.838)         (4.703)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.962           0.969           0.962           0.970   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s12.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi single_ad multi_ad akp_jun recruitment_pool     ///
> treatment_nonterror casualty_district kurdish_district akp_district)            ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models with interaction terms, constructed with \textit{AKP District}")
(output written to ./tables/table_s12.tex)

. 
. * remove the interaction variables ---------------------------------------------
. 
. drop single_ad multi_ad

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s13 -------------------------------------
. 
. do "./scripts/script_table_s13.do"

. * label the additional variable ------------------------------------------------
. label variable chp_jun             "Pre-test"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg chp_nov treatment chp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    4583.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9780
                                                Root MSE          =     2.3352

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         chp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |  -.1449105   .1900992    -0.76   0.448    -.5232198    .2333989
         chp_jun |   1.029361   .0096739   106.41   0.000     1.010109    1.048613
recruitment_pool |   .0176222   .0060415     2.92   0.005     .0055992    .0296452
           _cons |  -.6939713   .2133944    -3.25   0.002     -1.11864    -.269303
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg chp_nov treatment chp_jun recruitment_pool                          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =    2498.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9803
                                                Root MSE          =     2.2133

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            chp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.1192729   .2072487    -0.58   0.567     -.531711    .2931653
            chp_jun |   1.026623   .0140245    73.20   0.000     .9987139    1.054533
   recruitment_pool |  -.0036568   .0096274    -0.38   0.705     -.022816    .0155023
treatment_nonterror |   .7807895   .3549344     2.20   0.031     .0744475    1.487132
  casualty_district |   .4227321   .4745612     0.89   0.376    -.5216748    1.367139
   kurdish_district |    1.12591   .5150359     2.19   0.032     .1009558    2.150864
       akp_district |  -.4428865   .2075647    -2.13   0.036    -.8559534   -.0298195
   higher_education |   .1206027   .0248658     4.85   0.000     .0711183    .1700872
         min_margin |   .0587158   .0234596     2.50   0.014     .0120298    .1054019
        turnout_jun |   .0259211   .0255944     1.01   0.314    -.0250134    .0768555
              _cons |  -3.852184   2.211376    -1.74   0.085    -8.252962    .5485949
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg chp_nov treatment treatment_multi chp_jun recruitment_pool,         ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    3475.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9780
                                                Root MSE          =     2.3363

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         chp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   -.116399    .212081    -0.55   0.585    -.5384537    .3056558
 treatment_multi |  -.1827929   .3566303    -0.51   0.610    -.8925098     .526924
         chp_jun |   1.029417   .0096701   106.45   0.000     1.010172    1.048661
recruitment_pool |   .0179949   .0059509     3.02   0.003     .0061523    .0298375
           _cons |  -.6972532   .2131046    -3.27   0.002    -1.121345   -.2731615
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg chp_nov treatment treatment_multi chp_jun recruitment_pool          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =    2294.37
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9804
                                                Root MSE          =     2.2142

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            chp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.0768725   .2253322    -0.34   0.734     -.525298     .371553
    treatment_multi |  -.2762481   .4190638    -0.66   0.512    -1.110212    .5577153
            chp_jun |   1.026648   .0140115    73.27   0.000     .9987647    1.054532
   recruitment_pool |  -.0030948   .0096058    -0.32   0.748    -.0222109    .0160214
treatment_nonterror |   .7859667   .3527908     2.23   0.029     .0838906    1.488043
  casualty_district |   .4140765   .4752525     0.87   0.386     -.531706    1.359859
   kurdish_district |   1.125379   .5143084     2.19   0.032     .1018727    2.148885
       akp_district |  -.4450225   .2083107    -2.14   0.036    -.8595741   -.0304709
   higher_education |   .1208112   .0248847     4.85   0.000     .0712891    .1703333
         min_margin |   .0588949   .0234393     2.51   0.014     .0122492    .1055406
        turnout_jun |   .0257235   .0256293     1.00   0.319    -.0252805    .0767275
              _cons |  -3.839587   2.214316    -1.73   0.087    -8.246215    .5670425
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S13: Regression models of CHP vote share")

Table S13: Regression models of CHP vote share
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                  -0.145          -0.119          -0.116          -0.077   
                          (0.190)         (0.207)         (0.212)         (0.225)   

Multiple Treatment                                         -0.183          -0.276   
                                                          (0.357)         (0.419)   

Pre-test                    1.029***        1.027***        1.029***        1.027***
                          (0.010)         (0.014)         (0.010)         (0.014)   

Recruitment Pool            0.018**        -0.004           0.018**        -0.003   
                          (0.006)         (0.010)         (0.006)         (0.010)   

Non-terror Funeral                          0.781*                          0.786*  
                                          (0.355)                         (0.353)   

Attack District                             0.423                           0.414   
                                          (0.475)                         (0.475)   

Kurdish District                            1.126*                          1.125*  
                                          (0.515)                         (0.514)   

AKP District                               -0.443*                         -0.445*  
                                          (0.208)                         (0.208)   

Higher Education                            0.121***                        0.121***
                                          (0.025)                         (0.025)   

Electoral Margin                            0.059*                          0.059*  
                                          (0.023)                         (0.023)   

Turnout                                     0.026                           0.026   
                                          (0.026)                         (0.026)   

Constant                   -0.694**        -3.852          -0.697**        -3.840   
                          (0.213)         (2.211)         (0.213)         (2.214)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.978           0.980           0.978           0.980   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s13.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of CHP vote share")
(output written to ./tables/table_s13.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s14 -------------------------------------
. 
. do "./scripts/script_table_s14.do"

. * label the additional variable ------------------------------------------------
. label variable mhp_jun             "Pre-test"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg mhp_nov treatment mhp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =     278.29
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8708
                                                Root MSE          =     2.5248

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         mhp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |  -.0526856   .3371754    -0.16   0.876     -.723686    .6183149
         mhp_jun |   .7255141   .0262434    27.65   0.000     .6732881    .7777402
recruitment_pool |   .0163338   .0078461     2.08   0.041     .0007197     .031948
           _cons |  -.3816699   .3454491    -1.10   0.273    -1.069135    .3057956
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg mhp_nov treatment mhp_jun recruitment_pool                          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     202.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8872
                                                Root MSE          =     2.3672

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            mhp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   .0214544   .2881956     0.07   0.941    -.5520732     .594982
            mhp_jun |   .7231779   .0332925    21.72   0.000     .6569237    .7894321
   recruitment_pool |  -.0021777     .00971    -0.22   0.823    -.0215011    .0171458
treatment_nonterror |  -.1874868   .3617427    -0.52   0.606    -.9073777     .532404
  casualty_district |  -.0621107   .4220109    -0.15   0.883    -.9019391    .7777178
   kurdish_district |  -.3893216   .5230142    -0.74   0.459    -1.430153    .6515099
       akp_district |  -1.299031   .3148072    -4.13   0.000    -1.925517   -.6725447
   higher_education |   .0400413   .0276499     1.45   0.151    -.0149837    .0950662
         min_margin |  -.0951912    .034205    -2.78   0.007    -.1632614    -.027121
        turnout_jun |   .0500305    .036177     1.38   0.171    -.0219641     .122025
              _cons |  -3.451703   2.986243    -1.16   0.251    -9.394516    2.491111
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg mhp_nov treatment treatment_multi mhp_jun recruitment_pool,         ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =     235.60
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8712
                                                Root MSE          =     2.5213

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         mhp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |  -.2275827   .3454504    -0.66   0.512    -.9150509    .4598854
 treatment_multi |   1.176757    .902619     1.30   0.196    -.6195118    2.973026
         mhp_jun |   .7240924   .0255591    28.33   0.000     .6732281    .7749567
recruitment_pool |    .013695   .0087175     1.57   0.120    -.0036533    .0310433
           _cons |  -.3429519   .3290133    -1.04   0.300    -.9977093    .3118055
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg mhp_nov treatment treatment_multi mhp_jun recruitment_pool          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     194.03
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8876
                                                Root MSE          =      2.364

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            mhp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.1399065   .2996329    -0.47   0.642     -.736195    .4563819
    treatment_multi |   1.101082   .8389452     1.31   0.193    -.5684721    2.770636
            mhp_jun |   .7216208   .0325851    22.15   0.000     .6567745    .7864671
   recruitment_pool |  -.0046266   .0103065    -0.45   0.655    -.0251371     .015884
treatment_nonterror |  -.2048798    .362314    -0.57   0.573    -.9259076    .5161481
  casualty_district |  -.0297802   .4334193    -0.07   0.945    -.8923121    .8327518
   kurdish_district |  -.4047234   .5158285    -0.78   0.435    -1.431255     .621808
       akp_district |  -1.288194   .3133504    -4.11   0.000    -1.911782   -.6646072
   higher_education |   .0392676   .0274911     1.43   0.157    -.0154414    .0939765
         min_margin |  -.0955719   .0342136    -2.79   0.007    -.1636591   -.0274848
        turnout_jun |    .050803   .0358582     1.42   0.160     -.020557    .1221631
              _cons |  -3.475543    2.96854    -1.17   0.245    -9.383126     2.43204
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S14: Regression models of MHP vote share")

Table S14: Regression models of MHP vote share
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                  -0.053           0.021          -0.228          -0.140   
                          (0.337)         (0.288)         (0.345)         (0.300)   

Multiple Treatment                                          1.177           1.101   
                                                          (0.903)         (0.839)   

Pre-test                    0.726***        0.723***        0.724***        0.722***
                          (0.026)         (0.033)         (0.026)         (0.033)   

Recruitment Pool            0.016*         -0.002           0.014          -0.005   
                          (0.008)         (0.010)         (0.009)         (0.010)   

Non-terror Funeral                         -0.187                          -0.205   
                                          (0.362)                         (0.362)   

Attack District                            -0.062                          -0.030   
                                          (0.422)                         (0.433)   

Kurdish District                           -0.389                          -0.405   
                                          (0.523)                         (0.516)   

AKP District                               -1.299***                       -1.288***
                                          (0.315)                         (0.313)   

Higher Education                            0.040                           0.039   
                                          (0.028)                         (0.027)   

Electoral Margin                           -0.095**                        -0.096** 
                                          (0.034)                         (0.034)   

Turnout                                     0.050                           0.051   
                                          (0.036)                         (0.036)   

Constant                   -0.382          -3.452          -0.343          -3.476   
                          (0.345)         (2.986)         (0.329)         (2.969)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.871           0.887           0.871           0.888   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s14.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of MHP vote share")
(output written to ./tables/table_s14.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s15 -------------------------------------
. 
. do "./scripts/script_table_s15.do"

. * label the additional variable ------------------------------------------------
. label variable hdp_jun             "Pre-test"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg hdp_nov treatment hdp_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    1643.67
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9858
                                                Root MSE          =     2.5143

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         hdp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |  -.3743538   .2078149    -1.80   0.075    -.7879185     .039211
         hdp_jun |   .9062071   .0133001    68.14   0.000      .879739    .9326752
recruitment_pool |  -.0068757    .007302    -0.94   0.349     -.021407    .0076557
           _cons |  -.7404469   .1146409    -6.46   0.000    -.9685896   -.5123042
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg hdp_nov treatment hdp_jun recruitment_pool                          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =    1878.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9891
                                                Root MSE          =     2.2099

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            hdp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.2173761   .2300887    -0.94   0.348    -.6752672     .240515
            hdp_jun |   .9322227   .0168084    55.46   0.000     .8987728    .9656725
   recruitment_pool |  -.0252154   .0070578    -3.57   0.001    -.0392608     -.01117
treatment_nonterror |  -.0178971   .4361597    -0.04   0.967    -.8858827    .8500884
  casualty_district |   2.003031   .8271432     2.42   0.018      .356964    3.649099
   kurdish_district |  -2.863383   .9009732    -3.18   0.002    -4.656377    -1.07039
       akp_district |  -.6758112   .2002942    -3.37   0.001    -1.074409    -.277213
   higher_education |   .0012651   .0160775     0.08   0.937    -.0307301    .0332603
         min_margin |   .0161702   .0276649     0.58   0.561    -.0388846    .0712251
        turnout_jun |   .1531211   .0391071     3.92   0.000     .0752955    .2309468
              _cons |  -13.29659   3.377414    -3.94   0.000    -20.01786   -6.575321
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg hdp_nov treatment treatment_multi hdp_jun recruitment_pool,         ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1231.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9858
                                                Root MSE          =     2.5156

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
         hdp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |  -.3672845   .2279661    -1.61   0.111    -.8209516    .0863826
 treatment_multi |  -.0459585    .547832    -0.08   0.933    -1.136179    1.044262
         hdp_jun |   .9061963   .0133104    68.08   0.000     .8797076    .9326849
recruitment_pool |  -.0067773   .0074299    -0.91   0.364    -.0215633    .0080086
           _cons |  -.7408596   .1156865    -6.40   0.000     -.971083   -.5106361
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg hdp_nov treatment treatment_multi hdp_jun recruitment_pool          ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =    1839.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9891
                                                Root MSE          =      2.211

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
            hdp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.2330559   .2625664    -0.89   0.377    -.7555796    .2894678
    treatment_multi |   .1029117   .4489057     0.23   0.819    -.7904391    .9962626
            hdp_jun |   .9322561   .0168003    55.49   0.000     .8988224    .9656898
   recruitment_pool |   -.025434   .0067492    -3.77   0.000    -.0388654   -.0120027
treatment_nonterror |  -.0196402   .4349793    -0.05   0.964    -.8852766    .8459963
  casualty_district |    2.00581   .8288656     2.42   0.018     .3563148    3.655305
   kurdish_district |  -2.864463   .9001102    -3.18   0.002     -4.65574   -1.073187
       akp_district |  -.6746748   .2006119    -3.36   0.001    -1.073905   -.2754443
   higher_education |    .001193   .0161324     0.07   0.941    -.0309115    .0332975
         min_margin |   .0160977   .0277174     0.58   0.563    -.0390617    .0712572
        turnout_jun |   .1531713    .039123     3.92   0.000     .0753139    .2310286
              _cons |  -13.29982   3.378881    -3.94   0.000      -20.024   -6.575628
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S15: Regression models of HDP vote share")

Table S15: Regression models of HDP vote share
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                  -0.374          -0.217          -0.367          -0.233   
                          (0.208)         (0.230)         (0.228)         (0.263)   

Multiple Treatment                                         -0.046           0.103   
                                                          (0.548)         (0.449)   

Pre-test                    0.906***        0.932***        0.906***        0.932***
                          (0.013)         (0.017)         (0.013)         (0.017)   

Recruitment Pool           -0.007          -0.025***       -0.007          -0.025***
                          (0.007)         (0.007)         (0.007)         (0.007)   

Non-terror Funeral                         -0.018                          -0.020   
                                          (0.436)                         (0.435)   

Attack District                             2.003*                          2.006*  
                                          (0.827)                         (0.829)   

Kurdish District                           -2.863**                        -2.864** 
                                          (0.901)                         (0.900)   

AKP District                               -0.676**                        -0.675** 
                                          (0.200)                         (0.201)   

Higher Education                            0.001                           0.001   
                                          (0.016)                         (0.016)   

Electoral Margin                            0.016                           0.016   
                                          (0.028)                         (0.028)   

Turnout                                     0.153***                        0.153***
                                          (0.039)                         (0.039)   

Constant                   -0.740***      -13.297***       -0.741***      -13.300***
                          (0.115)         (3.377)         (0.116)         (3.379)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.986           0.989           0.986           0.989   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s15.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of HDP vote share")
(output written to ./tables/table_s15.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s16 -------------------------------------
. 
. do "./scripts/script_table_s16.do"

. * label the additional variable ------------------------------------------------
. label variable rw_jun              "Pre-test"

. 
. * run the regressions models and save them to memory ---------------------------
. 
. eststo: reg rw_nov treatment rw_jun recruitment_pool, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(3, 80)          =    1183.05
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9588
                                                Root MSE          =     4.3059

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
          rw_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .1248168   .4065868     0.31   0.760    -.6843167    .9339502
          rw_jun |   .9455632   .0161213    58.65   0.000     .9134808    .9776456
recruitment_pool |    .026844    .015183     1.77   0.081    -.0033712    .0570592
           _cons |   5.525116   1.206951     4.58   0.000     3.123207    7.927025
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg rw_nov treatment rw_jun recruitment_pool                            ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(10, 80)         =     754.29
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9730
                                                Root MSE          =     3.4993

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
             rw_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   .0276545    .353649     0.08   0.938    -.6761295    .7314385
             rw_jun |   .9653566   .0162856    59.28   0.000     .9329471     .997766
   recruitment_pool |   .0745367   .0133622     5.58   0.000      .047945    .1011284
treatment_nonterror |  -.8703605   .5659554    -1.54   0.128    -1.996648    .2559266
  casualty_district |   -1.20957   .8941527    -1.35   0.180     -2.98899    .5698508
   kurdish_district |   5.114397   .8976502     5.70   0.000     3.328016    6.900778
       akp_district |   1.366178   .3479491     3.93   0.000     .6737376    2.058619
   higher_education |  -.1848602   .0457964    -4.04   0.000    -.2759979   -.0937225
         min_margin |  -.1272108   .0547739    -2.32   0.023    -.2362142   -.0182073
        turnout_jun |  -.1741552   .0712072    -2.45   0.017    -.3158621   -.0324483
              _cons |   19.10271   6.299973     3.03   0.003     6.565361    31.64005
-------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: reg rw_nov treatment treatment_multi rw_jun recruitment_pool,           ///
> cluster(province)

Linear regression                               Number of obs     =        970
                                                F(4, 80)          =    1090.58
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9588
                                                Root MSE          =     4.3038

                                  (Std. Err. adjusted for 81 clusters in province)
----------------------------------------------------------------------------------
                 |               Robust
          rw_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .3473207     .43241     0.80   0.424    -.5132026    1.207844
 treatment_multi |  -1.446922   .9672369    -1.50   0.139    -3.371785    .4779411
          rw_jun |   .9457663   .0160505    58.92   0.000     .9138248    .9777079
recruitment_pool |   .0299378   .0147952     2.02   0.046     .0004944    .0593811
           _cons |   5.494951   1.201024     4.58   0.000     3.104837    7.885066
----------------------------------------------------------------------------------
(est3 stored)

. 
. eststo: reg rw_nov treatment treatment_multi rw_jun recruitment_pool            ///
> treatment_nonterror casualty_district kurdish_district akp_district             ///
> higher_education min_margin turnout_jun, cluster(province)

Linear regression                               Number of obs     =        970
                                                F(11, 80)         =     710.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9730
                                                Root MSE          =     3.4989

                                     (Std. Err. adjusted for 81 clusters in province)
-------------------------------------------------------------------------------------
                    |               Robust
             rw_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |   .1683392   .3755622     0.45   0.655    -.5790534    .9157317
    treatment_multi |   -.926443   .6284474    -1.47   0.144    -2.177093    .3242072
             rw_jun |   .9655505   .0163045    59.22   0.000     .9331035    .9979974
   recruitment_pool |   .0764504   .0129675     5.90   0.000     .0506442    .1022566
treatment_nonterror |  -.8541156   .5704709    -1.50   0.138    -1.989389    .2811575
  casualty_district |  -1.237244   .8965211    -1.38   0.171    -3.021378    .5468902
   kurdish_district |   5.116599   .9003327     5.68   0.000     3.324879    6.908318
       akp_district |   1.355017   .3481615     3.89   0.000     .6621532     2.04788
   higher_education |  -.1839162   .0459998    -4.00   0.000    -.2754587   -.0923737
         min_margin |  -.1266231   .0544603    -2.33   0.023    -.2350026   -.0182437
        turnout_jun |  -.1746144   .0712528    -2.45   0.016     -.316412   -.0328169
              _cons |     19.117   6.320068     3.02   0.003      6.53966    31.69433
-------------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Table S16: Regression models of right-wing vote share")

Table S16: Regression models of right-wing vote share
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
------------------------------------------------------------------------------------
Treatment                   0.125           0.028           0.347           0.168   
                          (0.407)         (0.354)         (0.432)         (0.376)   

Multiple Treatment                                         -1.447          -0.926   
                                                          (0.967)         (0.628)   

Pre-test                    0.946***        0.965***        0.946***        0.966***
                          (0.016)         (0.016)         (0.016)         (0.016)   

Recruitment Pool            0.027           0.075***        0.030*          0.076***
                          (0.015)         (0.013)         (0.015)         (0.013)   

Non-terror Funeral                         -0.870                          -0.854   
                                          (0.566)                         (0.570)   

Attack District                            -1.210                          -1.237   
                                          (0.894)                         (0.897)   

Kurdish District                            5.114***                        5.117***
                                          (0.898)                         (0.900)   

AKP District                                1.366***                        1.355***
                                          (0.348)                         (0.348)   

Higher Education                           -0.185***                       -0.184***
                                          (0.046)                         (0.046)   

Electoral Margin                           -0.127*                         -0.127*  
                                          (0.055)                         (0.054)   

Turnout                                    -0.174*                         -0.175*  
                                          (0.071)                         (0.071)   

Constant                    5.525***       19.103**         5.495***       19.117** 
                          (1.207)         (6.300)         (1.201)         (6.320)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
Clusters                       81         81         81         81   
r2                          0.959           0.973           0.959           0.973   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s16.tex",                                          ///
> replace se b(3) se(3) stats(N vce r2) label nodepvars nonotes nomtitles         ///
> order(treatment treatment_multi)                                                ///
> substitute(vce "Clusters" cluster "81")                                         ///
> title("Regression models of right-wing vote share")
(output written to ./tables/table_s16.tex)

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
. * source the script that creates table s17 -------------------------------------
. 
. do "./scripts/script_table_s17.do"

. * balance the treatment and control groups -------------------------------------
. 
. ebalance treatment recruitment_pool treatment_nonterror casualty_district       ///
> kurdish_district akp_district higher_education min_margin turnout_jun,          ///
> targets(1)


Data Setup
Treatment variable:   treatment
Covariate adjustment: recruitment_pool treatment_nonterror casualty_district kurdish_district akp_district higher_education min_margin turnout_jun 

Optimizing...
Iteration 1: Max Difference = 7240.12699
Iteration 2: Max Difference = 2661.23455
Iteration 3: Max Difference = 976.759948
Iteration 4: Max Difference = 357.092017
Iteration 5: Max Difference = 129.170745
Iteration 6: Max Difference = 45.43158
Iteration 7: Max Difference = 14.8810984
Iteration 8: Max Difference = 4.10205244
Iteration 9: Max Difference = .711016679
Iteration 10: Max Difference = .035421478
Iteration 11: Max Difference = .000102858
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 123     total of weights: 123
Control units: 847     total of weights: 123


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
recruitmen~l |     13.17      248.8      2.158 |      5.61      91.66      3.277 
treatment_~r |    .09756     .08876      2.713 |    .02834     .02756      5.685 
casualty_d~t |    .04065     .03932      4.652 |    .04959     .04718       4.15 
kurdish_di~t |     .1545      .1317      1.912 |     .1889      .1534       1.59 
akp_district |     .6504      .2292     -.6308 |     .6257      .2345     -.5197 
higher_edu~n |     8.423      26.03      2.099 |     7.103      17.64      2.262 
  min_margin |     4.272       21.2      1.777 |     4.812      27.35      2.072 
 turnout_jun |     85.63      14.25     -.7908 |     85.58      18.42      -.894 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
recruitmen~l |     13.17      248.8      2.158 |     13.17      334.2      1.637 
treatment_~r |    .09756     .08876      2.713 |    .09756     .08815      2.713 
casualty_d~t |    .04065     .03932      4.652 |    .04065     .03904      4.652 
kurdish_di~t |     .1545      .1317      1.912 |     .1545      .1308      1.912 
akp_district |     .6504      .2292     -.6308 |     .6504      .2276     -.6308 
higher_edu~n |     8.423      26.03      2.099 |     8.423      27.31      1.977 
  min_margin |     4.272       21.2      1.777 |     4.272      25.79      2.463 
 turnout_jun |     85.63      14.25     -.7908 |     85.63      15.67     -1.022 

. 
. * run the regressions models and save them to memory ---------------------------
. 
. svyset [pweight= _webal]

      pweight: _webal
          VCE: linearized
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>

. eststo: svy: reg chp_nov treatment treatment_multi chp_jun
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =        970
Number of PSUs     =       970                  Population size   =        246
                                                Design df         =        969
                                                F(   3,    967)   =    8725.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9805

---------------------------------------------------------------------------------
                |             Linearized
        chp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      treatment |  -.1836631   .2092115    -0.88   0.380    -.5942228    .2268967
treatment_multi |   .0437024   .3474874     0.13   0.900    -.6382121     .725617
        chp_jun |    1.03043   .0065164   158.13   0.000     1.017642    1.043218
          _cons |  -.4512495   .1501973    -3.00   0.003    -.7459991      -.1565
---------------------------------------------------------------------------------
(est1 stored)

. eststo: svy: reg mhp_nov treatment treatment_multi mhp_jun
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =        970
Number of PSUs     =       970                  Population size   =        246
                                                Design df         =        969
                                                F(   3,    967)   =     341.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8414

---------------------------------------------------------------------------------
                |             Linearized
        mhp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      treatment |  -.0592847    .300046    -0.20   0.843    -.6480995    .5295302
treatment_multi |   1.430699   .7984332     1.79   0.073    -.1361584    2.997556
        mhp_jun |   .7022549   .0234574    29.94   0.000     .6562218    .7482881
          _cons |    .086318   .3911423     0.22   0.825    -.6812656    .8539015
---------------------------------------------------------------------------------
(est2 stored)

. eststo: svy: reg hdp_nov treatment treatment_multi hdp_jun
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =        970
Number of PSUs     =       970                  Population size   =        246
                                                Design df         =        969
                                                F(   3,    967)   =    2624.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9832

---------------------------------------------------------------------------------
                |             Linearized
        hdp_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      treatment |  -.2535003   .2597795    -0.98   0.329    -.7632956    .2562949
treatment_multi |  -.2103545   .4963437    -0.42   0.672    -1.184387     .763678
        hdp_jun |   .8861312    .013605    65.13   0.000     .8594325    .9128299
          _cons |  -.7219765   .1736092    -4.16   0.000     -1.06267   -.3812831
---------------------------------------------------------------------------------
(est3 stored)

. eststo: svy: reg rw_nov treatment treatment_multi rw_jun
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =        970
Number of PSUs     =       970                  Population size   =        246
                                                Design df         =        969
                                                F(   3,    967)   =    1953.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9527

---------------------------------------------------------------------------------
                |             Linearized
         rw_nov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      treatment |    .479921   .4664184     1.03   0.304    -.4353855    1.395228
treatment_multi |  -1.089913   1.020228    -1.07   0.286    -3.092024     .912197
         rw_jun |    .925527   .0131989    70.12   0.000     .8996253    .9514288
          _cons |   7.086666   .8570738     8.27   0.000     5.404731    8.768601
---------------------------------------------------------------------------------
(est4 stored)

. 
. * print the results ------------------------------------------------------------
. 
. esttab,                                                                         ///
> replace se b(3) se(3) stats(N r2) label nodepvars nonotes nomtitles             ///
> mlabels(CHP MHP HDP RW)                                                         ///
> order(treatment treatment_multi)                                                ///
> rename(chp_jun "Pre-test" mhp_jun "Pre-test" hdp_jun "Pre-test"                 ///
> rw_jun "Pre-test")                                                              ///
> title("Table S17: Regression models of non-government party vote share, based on entropy balancing")

Table S17: Regression models of non-government party vote share, based on entropy balancing
------------------------------------------------------------------------------------
                              (1)             (2)             (3)             (4)   
                              CHP             MHP             HDP              RW   
------------------------------------------------------------------------------------
Treatment                  -0.184          -0.059          -0.254           0.480   
                          (0.209)         (0.300)         (0.260)         (0.466)   

Multiple Treatment          0.044           1.431          -0.210          -1.090   
                          (0.347)         (0.798)         (0.496)         (1.020)   

Pre-test                    1.030***        0.702***        0.886***        0.926***
                          (0.007)         (0.023)         (0.014)         (0.013)   

Constant                   -0.451**         0.086          -0.722***        7.087***
                          (0.150)         (0.391)         (0.174)         (0.857)   
------------------------------------------------------------------------------------
N                         970.000         970.000         970.000         970.000   
r2                          0.981           0.841           0.983           0.953   
------------------------------------------------------------------------------------

. 
. * save the results -------------------------------------------------------------
. 
. esttab using "./tables/table_s17.tex",                                          ///
> replace se b(3) se(3) stats(N r2) label nodepvars nonotes nomtitles             ///
> mlabels(CHP MHP HDP RW)                                                         ///
> order(treatment treatment_multi)                                                ///
> rename(chp_jun "Pre-test" mhp_jun "Pre-test" hdp_jun "Pre-test"                 ///
> rw_jun "Pre-test")                                                              ///
> title("Regression models of non-government party vote share, based on entropy balancing")
(output written to ./tables/table_s17.tex)

. 
. * remove the entropy balancing weights -----------------------------------------
. 
. drop _webal

. 
. * remove the models from memory ------------------------------------------------
. 
. eststo clear

. 
end of do-file

. 
end of do-file

. 
. log close
      name:  <unnamed>
       log:  [reducted]
  log type:  text
 closed on:   9 Apr 2021, 12:52:58
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
